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40 lines
1.8 KiB
40 lines
1.8 KiB
import torch
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import logging
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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class HuggingFaceLLM:
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def __init__(self, model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None):
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self.logger = logging.getLogger(__name__)
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self.device = device if device else ('cuda' if torch.cuda.is_available() else 'cpu')
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self.model_id = model_id
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self.max_length = max_length
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bnb_config = None
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if quantize:
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if not quantization_config:
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quantization_config = {
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'load_in_4bit': True,
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'bnb_4bit_use_double_quant': True,
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'bnb_4bit_quant_type': "nf4",
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'bnb_4bit_compute_dtype': torch.bfloat16
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}
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bnb_config = BitsAndBytesConfig(**quantization_config)
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try:
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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self.model = AutoModelForCausalLM.from_pretrained(self.model_id, quantization_config=bnb_config)
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self.model.to(self.device)
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except Exception as e:
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self.logger.error(f"Failed to load the model or the tokenizer: {e}")
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raise
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def generate_text(self, prompt_text: str, max_length: int = None):
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max_length = max_length if max_length else self.max_length
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try:
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inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to(self.device)
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with torch.no_grad():
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outputs = self.model.generate(inputs, max_length=max_length, do_sample=True)
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return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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except Exception as e:
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self.logger.error(f"Failed to generate the text: {e}")
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raise
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